Request for improved/updated beginner examples

I'm looking to use linfa for k-means clustering, and the current k-means example is pretty incomprehensible to a newbie. It may be that this makes perfect sense to someone steeped in this API or even in ndarray, but to me, the issues are:

• The current version of rand (0.9.0 as of Feb 2025) appears to be incompatible with the version used in the example
• Generating random data from a PRNG doesn't help when my goal is to load data from somewhere else. How can I create a mutable data structure that I can push new vectors onto?
• DatasetBase indicates that it contains records and maybe targets, weights, and feature names. I have no clue what the target/weights are when I'm trying to create input.
• Not having expected centroids, I'd like to lean on the API to either generate something random, something evenly distributed, or a use some quick heuristic otherwise.

Ultimately, my ideal is to do something like:

let mut records = Dataset::with_capacity(100_000); // expected number of input rows
for row in load_my_data("file.tsv") {
    // where 'row' is, say, a [f64; 5] or a Vec<f32>?
    records.push(row);
}

let initial_state = kmeans::generate_random_centroids(10 /* # clusters */, &records);

let clusters = kmeans::params_with(...).fit(&records);

for (id, cluster) in clusters.iter().enumerate() {
    // presumably cluster is [f64; 5] or &[f32]
    println!("Cluster {id} located @ {cluster:?}");
}

I realize this may diverge drastically from what currently exists, but I'd like to determine how to bridge this gap. Thanks!

Start with algorithms/linfa-clustering/examples/kmeans.rs and review the linked DatasetBase documentation. Run the existing example, then define a beginner-oriented example that explains loading rows, the meaning of targets and weights, centroid initialization, and the resulting clusters; done means the example is understandable without prior linfa or ndarray knowledge.